Nonparametric inference for ergodic, stationary time series
Morvai, Gusztáv ; Yakowitz, Sidney ; Györfi, László
Ann. Statist., Tome 24 (1996) no. 6, p. 370-379 / Harvested from Project Euclid
The setting is a stationary, ergodic time series. The challenge is to construct a sequence of functions, each based on only finite segments of the past, which together provide a strongly consistent estimator for the conditional probability of the next observation, given the infinite past. Ornstein gave such a construction for the case that the values are from a finite set, and recently Algoet extended the scheme to time series with coordinates in a Polish space. ¶ The present study relates a different solution to the challenge. The algorithm is simple and its verification is fairly transparent. Some extensions to regression, pattern recognition and on-line forecasting are mentioned.
Publié le : 1996-02-14
Classification:  Universal prediction schemes,  stationary ergodic process,  nonparametric regression,  60G10,  60G25,  62G05
@article{1033066215,
     author = {Morvai, Guszt\'av and Yakowitz, Sidney and Gy\"orfi, L\'aszl\'o},
     title = {Nonparametric inference for ergodic, stationary time series},
     journal = {Ann. Statist.},
     volume = {24},
     number = {6},
     year = {1996},
     pages = { 370-379},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1033066215}
}
Morvai, Gusztáv; Yakowitz, Sidney; Györfi, László. Nonparametric inference for ergodic, stationary time series. Ann. Statist., Tome 24 (1996) no. 6, pp.  370-379. http://gdmltest.u-ga.fr/item/1033066215/